Prediction of coronary angiography requirement of patients with Fuzzy Logic and Learning Vector Quantization

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dc.contributor.authorAkbulut, Harun
dc.contributor.authorBarisci, Necaattin
dc.contributor.authorArinc, Huseyin
dc.contributor.authorTopal, Taner
dc.contributor.authorLuy, Murat
dc.date.accessioned2020-06-25T18:07:31Z
dc.date.available2020-06-25T18:07:31Z
dc.date.issued2013
dc.departmentKırıkkale Üniversitesi
dc.description10th International Conference on Electronics, Computer and Computation (ICECCO) -- NOV 07-09, 2013 -- Turgut Ozal Univ, Ankara, TURKEY
dc.descriptionLUY, Murat/0000-0002-2378-0009
dc.description.abstractIn this study, prediction of coronary angiography (CA) requirement of patients is presented using Fuzzy Logic (FL) and Learning Vector Quantization (LVQ). Data sets of patients are received from 200 patients, half of whom undergo CA, the other half doesn't undergo CA, the numbers of both men and women patients are selected equal. Input data sets and output data sets are determined and tested for FL. The correct classification rate of FL is measured 86% for prediction of CA requirement of patients. Training data sets and testing data sets are determined and tested for LVQ. The correct classification rate of LVQ is measured 88% for prediction of CA requirement of patients. These results show that LVQ is more effective than FL at prediction of CA requirement of patients.en_US
dc.description.sponsorshipInst Elect & Elect Engineersen_US
dc.identifier.citationclosedAccessen_US
dc.identifier.endpage4en_US
dc.identifier.isbn978-1-4799-3343-3
dc.identifier.scopus2-s2.0-84894183123
dc.identifier.scopusqualityN/A
dc.identifier.startpage1en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12587/5598
dc.identifier.wosWOS:000336616500001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIeeeen_US
dc.relation.ispartof2013 International Conference On Electronics, Computer And Computation (Icecco)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFuzzy Logic (FL)en_US
dc.subjectLearning Vector Quantization (LVQ)en_US
dc.subjectCoronary Artery Disease (CAD)en_US
dc.subjectCoronary Angiography (CA)en_US
dc.titlePrediction of coronary angiography requirement of patients with Fuzzy Logic and Learning Vector Quantizationen_US
dc.typeConference Object

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